Differences and similarities

In Stephenson’s foreword to Brown’s book Political Subjectivity (Brown, 1980), he pointed to the fundamental differences between objective and subjective as a matter of self-reference, and claimed that “modern science has prospered by eliminating whims and arbitrary subjectivities from its fact-finding missions into the world “outside”. Q methodology follows the same prescriptions for what we consider “inside” us, matters of mind, consciousness, wishes and emotions, and it does so in terms of theories, universals, and laws, precisely as for modern physics.” (p. x). What is involved, according to Stephenson, is “ the discovery of hypothesis and reaching understandings, instead of testing hypotheses by way of predictability and falsifiability” (p. x).

Factor analysis was invented by Spearman and contributed to by Burt, Thomson and Thurstone. This was initially a procedure for studying differences between traits (R-method) (Brown, 1980). Correlation and factor analysis are used in Q-method as well, but Stephenson had something different in mind. While R-method mostly focuses on individual differences between people, like individual a has more of trait A than individual b, in Q-method the focus is on the subjectivity of each individual such as individual a valuing trait A more than trait B. This is something completely different from the reanalysis of a transposed R matrix (Brown, 1980).

Brown (1980) called attention to basic phenomena that differentiates between R and Q. R has its focus on traits, attributes, or characteristics which are thought to be objective and measurable for all people in the population, and efforts are made to break a phenomenon down to its smaller parts. In this case the whole is thought of as the sum of its simpler parts.

In Q it is the whole response that is of interest (viewpoint, concentration, image, etc) and is presumed to be irreducible and stems from the person giving it. This approach preserves the functional relationships between the parts within the context of the whole (p 173).

Brown (1980) summed up some of the important differences in this way (p.

322):

“…R method focuses on what is objectively measurable about a person in comparison with the other n persons in the sample, all of whom are equally measurable and only differ in quantitative degree. What is unique to the person, apart from what he is being tested for, is included in the error term. In the R-methodological approach to human behavior, therefore, subjectivity is random and accidental.

…Q method focuses on the subjective significance to a person of a statement in terms of the relative importance given it compared to other N statements in the sample. What is unique to the statement itself as an object, i.e., objectively and apart from what the individual does with it, is included in the error term. In Q-methodological approach to human behavior, therefore, objectivity is random and accidental!”

In R, outliers tend to disturb the picture and skew the results and are sometimes removed. In Q, such results are looked upon as a person’s unique point of view, and a display of his or her subjectivity and could be especially important if the diverging view for example stems from the leader of the organization.

According to Brown (1980, pp. 132-133), scaling methodologies assume everyone to have all traits to some measurable degree, examining the positive aspects of a phenomenon and generally use a range from most to least. The mean in R, therefore, has weight, symbolizing an average amount of the trait.

In Q the scale ranges from most to most, with extremes being of equal

significance and the middle being neutral or unimportant. In Q the mean is weightless and the continuum reflects the positive side of a variable, but also the relationship to the opposite. Ernest (2001) claims that the traditional R-methodological approach to research is based on mechanistic and reductionistic principles which focuses on the properties of the objects, items, or statements under investigation. In contrast to this, “Q-methodology explores a person’s perceptions of the objects, as a person compares all the objects, items, or statements in relation to each other” (p. 349).

Several have accounted for the misunderstandings and misinterpretations while using Q- method (Brown, 1980, 1997; McKeown & Thomas, 1988;

Stephenson, 1953; Watts & Stenner, 2005). Watts and Stenner (2005) called attention to the tendency to erroneously (mis)identify Q-methodological factor analysis with its more familiar R-methodological incarnation which is a statistical method of data reduction focusing on identifying and combining sets of dependent variables measuring similar things (p.68). Watts and Stenner conclude that Q-methodology makes no such psychometric claim: “the method employs a by-person factor analysis in order to identify groups of participants who make sense of (and who hence Q ‘sort’) a pool of items in comparable ways. Nothing more complicated is at issue” (p.68).

Differences between Q and R have been pointed to mostly because there has been so much misunderstanding especially concerning the use of Q-technique and the philosophy behind the methodology. Q-methodology draws upon both quantitative and qualitative approaches (Corr, 2006). It can be useful to view essential aspects concerning quantitative and qualitative methods, and how these are similar or differ from Q-methodology. For this I turn to Corr’s (2006) outline of the comparison of Q-methodology with qualitative and quantitative methods in the following table 1 (p. 392):

Table 1 – Comparison of Q Methodology with Qualitative and Quantitative Methods Issue Quantitative Methods Q Methodology Qualitative

Methods Purpose Identify changes or

characteristics in

Data collection Objective measuring and recording of

Analysis Uses descriptive and inferential statistics

Issue Quantitative Methods Q Methodology Qualitative Methods Interpretation Reports results as

statistical findings

It is important to be aware of differences between research traditions to help make informed choices of which method or mixture of methods that can be of help in collecting data in accordance with the purpose of the study. It can be equally important to be aware of similarities. As noted earlier, accentuating differences and continue to polarize research traditions may not be that helpful and can limit and distort results of research findings (Ercikan & Roth, 2006; Johnson & Onwuegbzie, 2004; Lund, 2005; Teddlie & Tashakkori, 2003). Yu (2002, p. 28) encourages researchers to “keep an open mind to different methodologies, while retaining skepticism to examine their philosophical assumptions of various research methodologies instead of unquestioningly accepting popular myths”.

Should a theory always stay the same or is there room for it to develop and change? A similar question was addressed by Hurd and Brown (2004) when they studied the future of the Q-methodology movement. The sharpest differences of opinions that they reported were between “whether Q should be further explored as a full scientific theory of subjectivity in the tradition of Stephenson or whether its impact should be in its practical applications to research problems and its engagement of alternative epistemologies that may force Q to evolve” (p.10). Hurd and Brown also recognized the dilemma between safeguarding things of value and obtaining new things of value, which consecutively depends on the ability to recognize value.

Differences in research methods have been pointed out, but there are also similarities according to more recent literature. Johnson & Onwuegbuzie

(2004) advocating for mixed methods research, view today’s research world as increasingly more interdisciplinary, complex and dynamic. They suggest complementing one method with another and that all researchers “need a solid understanding of multiple methods used by other scholars to facilitate communication, to promote collaboration, and to provide superior research”

(p. 15). In addition they list some issues in which many qualitative and quantitative researchers have reached basic agreement. A shortened version is listed below (Johnson & Onwuegbzie, 2004, p. 16):

a) what appears reasonable can vary across persons

b) observation is not a perfect and direct window into “reality”

c) it is possible for more than one theory to fit a single set of empirical data

d) a hypothesis is embedded in a holistic network of beliefs and alternative explanations will continue to exist.

e) the future may not resemble the past

f) researchers are embedded in communities and they clearly have and are affected by their attitudes, values, and beliefs

g) human beings can never be completely value free, and values affect what we choose to investigate, what we see, and how we interpret what we see

There can be other commonalities as well. Research questions can be addressed by empirical observation by both quantitative and qualitative researchers (Johnson & Onwuegbzie, 2004). Although they may have different philosophical assumptions, and ways of collecting and making sense of their data, Sechrest and Sidani (1995, p. 78) point to aims they have in common, such as to “describe their data, construct explanatory arguments from their data, and speculate about why the outcomes they observed happened as they did”. Researchers from both traditions also integrate safeguards into their inquiries as a precaution against sources of invalidity or lack of trustworthiness which potentially may exist in any research study (Johnson & Onwuegbzie, 2004). Stephenson also argued for a combination of

qualitative and quantitative aspects and said “there can be unity in science, provided objective and subjective parts are granted, each rooted in quantum theoretical concepts” (Stephenson, 1986d, p. 529).

In general R methodology and quantitative methods require the use of standardized measures so varying perspectives and experiences of many different people can “fit into a limited number of predetermined response categories to which numbers are assigned” (Patton, 2002, p. 14). In this way it is possible to measure the reactions of many individuals, and this gives broad and generalized information that can be presented economically in a few words. Qualitative methods in general give us a wide range of detailed information about a smaller number of individuals. This can increase our understanding of the issues being studied, but reduces generalizability (Patton, 1990; Thagaard, 2003). This type of data is usually presented through richly detailed descriptions in contrast to the more parsimonious practice with quantitative data. Both traditions have strengths and weaknesses, but according to Patton (2002, p. 14) “they constitute alternative, but not mutually exclusive, strategies for research. Both quantitative and qualitative data can be collected in the same study”. Lund (2005) addresses some stated discrepancies between qualitative and quantitative research by examining actual studies. The differences between the two traditions have often been exaggerated. He claims they do not represent two paradigms but one, based on critical realism and combined within a common frame in empirical research.

Lund also accentuates that “research problems are not answered by empirical results directly, but by conclusions based on such results” (2005, p. 120).

Johnson and Onwuegbuzie (2004) suggest mixed methods research, not to replace quantitative or qualitative research traditions, but to draw from the strengths and minimize weaknesses in both. They view pragmatism as an attractive philosophical partner and framework for mixed methods research and advocate consideration for the pragmatic method of Charles Sanders Peirce, William James, and John Dewey, the classical pragmatists. These are only a few examples of the increasing body of scientific literature that focuses on not polarizing quantitative and qualitative traditions but to combine their strengths and to become more aware of communalities instead of focusing solely on differences.

In this light it is quite interesting to see the work by William Stephenson more than 70 years ago, when he combined sophisticated statistical analysis with qualitative aspects in Q-methodology, and was so misunderstood. What he understood so clearly many years ago, the rest of the world needed some extra time to catch up on. Q-methodology combines qualities from both quantitative and qualitative traditions, and has done so from the very start. Its focus is on subjectivity and how the individual values some issues higher than others in comparison to the presented statements as a whole and communicated through the language of feelings, more so than by examining facts. In addition it depicts shared communicability among participants on topics in a certain context or culture. Statistics are used in analysing the data, but it also relies on the researchers’ ability to tell the story of the participants’ emerging viewpoints (Brown, 1980; Stephenson, 1953).